Showing 1,961 - 1,980 results of 2,873 for search 'improve ((((coot OR cost) OR post) OR most) OR root) optimization algorithm', query time: 0.29s Refine Results
  1. 1961

    INFO-RF-based fault diagnosis and analysis method for busbars by Chen Xue, Jian Zhu, Haiou Cao, Yan Gu, Siyu Chen

    Published 2025-07-01
    “…A simulation model of a dual-busbar power system is first established, and key electrical quantities such as differential current, bus tie current, and voltage are extracted to quantify fault features using Root Mean Square (RMS) values. The RF model is then used to predict fault types and fault resistance, with the INFO algorithm iteratively optimizing the hyperparameters of the RF model to further improve prediction accuracy. …”
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    Article
  2. 1962
  3. 1963

    Faults Detection and Diagnosis of a Large-Scale PV System by Analyzing Power Losses and Electric Indicators Computed Using Random Forest and KNN-Based Prediction Models by Yasmine Gaaloul, Olfa Bel Hadj Brahim Kechiche, Houcine Oudira, Aissa Chouder, Mahmoud Hamouda, Santiago Silvestre, Sofiane Kichou

    Published 2025-05-01
    “…Accurate and reliable fault detection in photovoltaic (PV) systems is essential for optimizing their performance and durability. This paper introduces a novel approach for fault detection and diagnosis in large-scale PV systems, utilizing power loss analysis and predictive models based on Random Forest (RF) and K-Nearest Neighbors (KNN) algorithms. …”
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    Article
  4. 1964
  5. 1965

    Multi-Agent Mapping and Tracking-Based Electrical Vehicles with Unknown Environment Exploration by Chafaa Hamrouni, Aarif Alutaybi, Ghofrane Ouerfelli

    Published 2025-03-01
    “…When rapid deliveries are required, the algorithm prioritizes faster routes, whereas, for flexible schedules, it optimizes energy conservation. …”
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    Article
  6. 1966

    Research on path planning for coal mine rescue robots by ZHU Hongbo, YIN Hongliang

    Published 2024-12-01
    “…Firstly, an adjustment mechanism of collision constraint function is incorporated into the bidirectional A* algorithm to improve path safety. Next, a correction factor is incorporated into the cost function of the Bidirectional A* algorithm to ensure that the forward and backward search paths intersect, preventing them from diverging. …”
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    Article
  7. 1967

    Multi-strategy fusion binary SHO guided by Pearson correlation coefficient for feature selection with cancer gene expression data by Yu-Cai Wang, Hao-Ming Song, Jie-Sheng Wang, Xin-Ru Ma, Yu-Wei Song, Yu-Liang Qi

    Published 2025-03-01
    “…Firstly, the CEC-2022 test functions were used to test the performance of the multi-strategy fusion SHO, from which the best variant TanASSHO was selected, and then compared with other nine swarm intelligent optimization algorithms. Performance tests of various algorithm variants on 18 UCI datasets show that V1PTASSHO is the most effective binary version. …”
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    Article
  8. 1968

    SC-Route: A Scalable Cross-Layer Secure Routing Method for Multi-Hop Inter-Domain Wireless Networks by Yanbing Li, Yang Zhu, Shangpeng Wang

    Published 2025-05-01
    “…Second, we introduce a cross-layer information fusion mechanism that allows nodes to adapt routing costs in real time under heterogeneous network conditions, thereby improving path reliability and load balancing. …”
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    Article
  9. 1969

    Lossless Compression with Trie-Based Shared Dictionary for Omics Data in Edge–Cloud Frameworks by Rani Adam, Daniel R. Catchpoole, Simeon J. Simoff, Zhonglin Qu, Paul J. Kennedy, Quang Vinh Nguyen

    Published 2025-04-01
    “…The preprocessed data are subsequently transmitted to the cloud, where advanced compression algorithms, such as Zstd, GZIP, Snappy, and LZ4, further compress them. …”
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    Article
  10. 1970

    SABO-ELM model for remaining life prediction of lithium-ion batteries under multiple health factors by Jiabo LI, Zhonglin SUN, Di TIAN, Zhixuan WANG

    Published 2025-06-01
    “…The SABO algorithm optimizes the weights and bias thresholds of the ELM model, which effectively reduces the risk of local optima and improves its predictive performance and stability. …”
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    Article
  11. 1971

    New method for landslide susceptibility evaluation in alpine valley regions that considers the suitability of InSAR monitoring and introduces deformation rate grading by Dingyi Zhou, Zhifang Zhao, Wenfei Xi, Xin Zhao, Jiangqin Chao

    Published 2025-03-01
    “…The landslide susceptibility model is developed utilizing the Particle Swarm Optimization-Back Propagation (PSO-BP) algorithm. It includes evaluation techniques for regions without deformation rates. …”
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    Article
  12. 1972

    Interactive online learning method for students based on artificial intelligence by Cizhang Li, Wenfen Yin

    Published 2025-08-01
    “…This study proposes a novel approach that integrates the Dwarf Mongoose Optimization (DMO) algorithm with a Gated Recurrent Unit (GRU) neural network to develop an AI-powered interactive online learning model. …”
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    Article
  13. 1973
  14. 1974

    Vibration control and noise attenuation strategies via acoustic metamaterial in railway transportation: a state-of-the-art review by Cong Wang, Guifeng Wang, Zhenyu Chen, C. W. Lim, Weiqiu Chen

    Published 2025-07-01
    “…Additionally, integrating optimization algorithms and artificial intelligence (AI) enhances AMM design precision and scalability. …”
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    Article
  15. 1975

    Smart agriculture using IoT for automated irrigation, water and energy efficiency by Subir Gupta, Subrata Chowdhury, Ramya Govindaraj, Kassian T.T. Amesho, Sumarlin Shangdiar, Timoteus Kadhila, Sioni Iikela

    Published 2025-12-01
    “…This study presents an innovative smart agriculture system that integrates Internet of Things (IoT) technologies, predictive algorithms, and automated control mechanisms to optimize irrigation and enhance resource efficiency. …”
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    Article
  16. 1976

    Electric Vehicle Cluster and Scheduling Strategy Based on Dynamic Game by LI Shuai, DING Xiying, JIANG Hongfa

    Published 2023-04-01
    “…The upper layer takes the peak shaving demand and peak shaving cost of distribution system operator (DSO) as the optimization objectives, and uses an improved multi-objective particle swarm optimization algorithm to obtain the game strategy set of DSO. …”
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    Article
  17. 1977

    Fractional-Order Swarming Intelligence Heuristics for Nonlinear Sliding-Mode Control System Design in Fuel Cell Hybrid Electric Vehicles by Nabeeha Qayyum, Laiq Khan, Mudasir Wahab, Sidra Mumtaz, Naghmash Ali, Babar Sattar Khan

    Published 2025-06-01
    “…This framework integrates moth flame optimization (MFO) with the gravitational search algorithm (GSA) and Fractal Heritage Evolution, implemented through three spiral-based variants: MFOGSAPSO-A (Archimedean), MFOGSAPSO-H (Hyperbolic), and MFOGSAPSO-L (Logarithmic). …”
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    Article
  18. 1978

    Control-Oriented Real-Time Trajectory Planning for Heterogeneous UAV Formations by Weichen Qian, Wenjun Yi, Shusen Yuan, Jun Guan

    Published 2025-01-01
    “…Inspired by Model Predictive Control (MPC), in the trajectory planning stage, the method generates multi-step trajectory points using an improved artificial potential field (APF) method, estimates the actual formation trajectory using the prediction network, and optimizes the trajectory through a multi-objective particle swarm optimization (MOPSO) algorithm after evaluating the planning costs. …”
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    Article
  19. 1979

    Short-term Wind Power Forecasting Based on BWO‒VMD and TCN‒BiGRU by LU Jing, ZHANG Yanru, WANG Rui

    Published 2025-05-01
    “…Short-term wind power forecasting helps improve grid stability, optimize wind farm power generation plans, and reduce operating costs, enhancing the economic benefits of wind power and supporting the goals of low-carbon development. …”
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    Article
  20. 1980

    Advanced Machine Learning Methodology for Earthquake Magnitude Forecasting Using Comprehensive Seismic Data by Subhieh El-Salhi, Bashar Igried, Sari Awwad

    Published 2026-01-01
    “…Feature selection was performed using Genetic Algorithm, Particle Swarm Optimization, and Simulated Annealing, while ten machine learning models were implemented — ranging from Linear Regression and Decision Trees to Gradient Boosting, XGBoost, LightGBM, and Long Short-Term Memory (LSTM) networks. …”
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